{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-09-14T09:30:40.782091400Z",
     "start_time": "2023-09-14T09:30:39.307390700Z"
    }
   },
   "outputs": [],
   "source": [
    "# -*- coding:utf-8 -*-\n",
    "# Author:凌逆战 | Never\n",
    "# Date: 2023/8/12\n",
    "\"\"\"\n",
    "画出scipy.signal.butter函数的滤波器响应曲线\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "from scipy.signal import butter, freqz\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x500 with 4 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_filter_response(b, a, title, subplot, cutoff=None, lowcut=None, highcut=None):\n",
    "    w, h = freqz(b, a, worN=8000)\n",
    "    subplot.plot(0.5*fs*w/np.pi, np.abs(h), 'b')\n",
    "    if cutoff:\n",
    "        subplot.axvline(cutoff, color='r', ls='--')\n",
    "    if lowcut:\n",
    "        subplot.axvline(lowcut, color='r', ls='--')\n",
    "    if highcut:\n",
    "        subplot.axvline(highcut, color='r', ls='--')\n",
    "    subplot.set_title(title)\n",
    "    subplot.set_xlabel('Frequency (Hz)')\n",
    "    subplot.set_ylabel('Gain')\n",
    "    subplot.grid(True)\n",
    "\n",
    "fs = 6000.0  # Sample rate (Hz)\n",
    "cutoff = 1000.0  # Desired cutoff frequency (Hz)\n",
    "\n",
    "fig, axs = plt.subplots(1, 4, figsize=(20, 5))\n",
    "\n",
    "# Lowpass filter\n",
    "b, a = butter(5, cutoff / (0.5 * fs), btype='low')\n",
    "plot_filter_response(b, a, 'Lowpass filter', axs[0], cutoff=cutoff)\n",
    "\n",
    "# Highpass filter\n",
    "b, a = butter(5, cutoff / (0.5 * fs), btype='high')\n",
    "plot_filter_response(b, a, 'Highpass filter', axs[1], cutoff=cutoff)\n",
    "\n",
    "# Bandpass filter\n",
    "lowcut = 500.0\n",
    "highcut = 1500.0\n",
    "b, a = butter(5, [lowcut / (0.5 * fs), highcut / (0.5 * fs)], btype='band')\n",
    "plot_filter_response(b, a, 'Bandpass filter', axs[2], lowcut=lowcut, highcut=highcut)\n",
    "\n",
    "# Bandstop filter\n",
    "b, a = butter(5, [lowcut / (0.5 * fs), highcut / (0.5 * fs)], btype='bandstop')\n",
    "plot_filter_response(b, a, 'Bandstop filter', axs[3], lowcut=lowcut, highcut=highcut)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
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     "end_time": "2023-09-14T09:30:42.503794500Z",
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